Kinesthetic Perception: A Machine Learning Approach

146 Pages·2018·5.93 MB·English

by  Subhasis Chaudhuri

Studies in Computational Intelligence 748
Subhasis Chaudhuri
Amit Bhardwaj
Kinesthetic
Perception
A Machine Learning Approach
Studies in Computational Intelligence
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Subhasis Chaudhuri Amit Bhardwaj
(cid:129)
Kinesthetic Perception
A Machine Learning Approach
123
SubhasisChaudhuri Amit Bhardwaj
Department ofElectrical Engineering Department ofElectrical Engineering
Indian Institute of Technology Bombay Indian Institute of Technology Bombay
Mumbai,Maharashtra Mumbai,Maharashtra
India India
ISSN 1860-949X ISSN 1860-9503 (electronic)
Studies in Computational Intelligence
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To
Syomantak and Ushasi
Subhasis Chaudhuri
My beloved family
Amit Bhardwaj
Preface
Haptics as an area of research has picked up a great momentum in the last two
decades. The primary reason for such a proliferation of research is due to gradual
andcontinuousdevelopmentinmechatronics,makingsuchdevicesavailabletothe
scientific community. A few such systems are now available for gaming and
medicalpurposes. Weexpect a wider acceptance ofsuch developments in practice
as more and more such devices come into the market. However, all such devices
currently work as stand-alone boxes and interoperability among these devices
would be the key component in future developments. A proper standardization
effort is required to provide this interoperability.
Oneofthemajorapplicationsforeseenforhapticsisinteleoperation.Beingable
toperceivetheforcesattheteleoperatorendwillprovideagreatboostinimproving
theperformanceoftheoperator.Researchershavebeenworkingonvariousaspects
of designing a good teleoperation system that involves design of manipulators,
kinesthetic and tactile sensing, data communication, delay compensating con-
trollers,andimmersionintothevirtualworkspace.Thedatacommunicationmodule
requiresmeetingquitearestrictivequalityofserviceguarantee.Thisrequirementis
veryseverefordelay-sensitivehapticdata.ThecurrentlyavailableInternetisoften
unabletomeetthedemand.WiththeintroductionoftactileInternet,weexpectthis
constraint to gradually ease out in future. Notwithstanding haptic data communi-
cation shall continue to be amajor issue in any teleoperationsystemovera shared
network.Thusanappropriatehapticcompressionengineisrequiredtobeapartof
such a teleoperation system.
How does one compress the haptic data without affecting the immersion of the
operator into the virtual world? This brings in the question of the effect of data
compression on haptic perception. Any perception-aware data compression tech-
nique utilizes the fact that a small change in the stimulus is often non-perceivable.
Thus,whenthetemporalvariationinthehapticsignalisrelativelyverysmall,these
data samples need not be transmitted. Several researchers have proposed different
adaptive sampling strategies for haptic data and have demonstrated that a sub-
stantial reduction in data rate can be achieved. However, all these techniques
requirethedeterminationoftheperceptualthresholdwhichisalwaysdependenton
vii
viii Preface
theperceptualabilitiesofanindividual.Theprimarymotivationforthismonograph
is to design a methodology to estimate the subject-specific perceptual threshold.
However,insteadofconsideringbothkinestheticandtactileperceptions,thestudies
are limited to kinesthetic perception only.
Thepsychophysicsofhumanperceptionisaclassicalareaofresearchandhasa
firm foundation on methodical study of determining the perceptual threshold.
However, such studies are quite limited as regards to analyzing kinesthetic per-
ception since it is only very recently that such mechatronic devices are available
which can exert a given amount of force with a reasonably good accuracy. In
parallel, there has been a substantialgrowthin research inthe seemingly unrelated
areaofmachinelearningthatoffersanumberofexcellentdata-driventoolstoarrive
at a decision or to estimate certain quantities without one having to define a
functional or parametric relationship. Although a functional relationship may offer
to estimate the unknown quantity very efficiently, it may suffer from the assump-
tions,includingthoseonthedistributionofthemeasurements,wheninappropriate.
However, the use of machine learning techniques requires generation of a large
number of ground-truthed data. In this monograph, we demonstrate how the
recently developed machine learning techniques can be used to determine the
perceptual thresholds. Thus, the purpose of the monograph is to provide an engi-
neering perspective on how some of the traditional problems in classical psy-
chophysics can be solved. Quite naturally, we were required to generate a huge
corpus of human response data for various types of kinesthetic stimuli.
The book is addressed to a fairly broad audience. It is meant for graduate
studentsstudyingthesubjectsofhaptics,systemscience,andvirtualreality.Itmay
also serve as a reference book for scientists working in the area of human per-
ception. For the benefit of such scientists, we plan to make all collected data
availableforfurtherresearch.Needlesstosay,themonographwillbeofgreatuseto
the practitioners developing various types of teleoperation systems. A basic
familiarityofthereaderswithmachinelearningwouldhelpinbetterunderstanding
of the book.
We shall be very happy to receive comments and suggestions from the readers.
Mumbai, India Subhasis Chaudhuri
July 2017 Amit Bhardwaj
Acknowledgements
ThefirstauthorisindebtedtoProf.EckehardSteinbachattheTechnicalUniversity
of Munich (TUM), Germany for introducing him to the fascinating research area
of haptics during several of his visits to TUM. Both the authors are thankful to
Dr. Onkar Dabeer for many insightful discussions at the initial phases of research
onthecurrenttopicandforhiscontributionsindevelopingthecontentsofChap.3
of this monograph. Thanks are also due to Prof. Abhishek Gupta of Mechanical
EngineeringDepartmentatIITBombayforhiscommentsandsuggestions.Weare
also grateful to Prof. Debraj Chakraborty and Prof. V. Rajbabu of Electrical
Engineering Department at IIT Bombay for their constructive comments.
Weofferourmostsinceregratitudetoalargenumberofvolunteerswhohappily
complied with our requests in participating as subjects and spent hours and hours
of their valuable time to help us generate a large amount of labeled data. Without
their help, we could not have dared to take up this study.
A few figures in the monograph have appeared in some of our publications
elsewhere. We are thankful to ACM, Springer, and IEEE for allowing us to reuse
the figures.
We are thankful to various sources of funding: JC Bose Fellowship, National
Programme on Perception Engineering, Indian Digital Heritage Project, Alexander
von Humboldt Fellowship, and Bharti Centre for Communication.
Finally, our acknowledgment is not complete unless we thank our family
members for their constant support and encouragements.
Mumbai, India Subhasis Chaudhuri
July 2017 Amit Bhardwaj
ix
Contents
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Basics of Haptics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1.1 Various Research Areas in Haptics. . . . . . . . . . . . . . . . . . 2
1.1.2 Possible Applications. . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2 Kinesthetic Perception . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.3 Perception: Aware Engineering Design . . . . . . . . . . . . . . . . . . . . 8
1.4 Organization of the Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2 Perceptual Deadzone. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.1 Haptic Data Compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.2 Perceptual Deadzone for Multidimensional Signals. . . . . . . . . . . . 21
2.3 Effect of Rate of Change of Kinesthetic Stimuli. . . . . . . . . . . . . . 24
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3 Predictive Sampler Design for Haptic Signals. . . . . . . . . . . . . . . . . . 29
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.2 Experimental Setup. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.2.1 Device Setup. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.2.2 Signal Characteristics. . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.2.3 Lag in User Response . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.2.4 Collected Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.3 Classification of Haptic Response . . . . . . . . . . . . . . . . . . . . . . . . 33
3.3.1 Performance Metric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.3.2 Weber Classifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.3.3 Level Crossing Classifier . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.3.4 Classifiers Based on Decision Tree and Random
Forests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.3.5 Effect of Temporal Spacing . . . . . . . . . . . . . . . . . . . . . . . 46
3.3.6 Significance Test for Classifiers . . . . . . . . . . . . . . . . . . . . 47
xi